{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "from scipy.stats import binned_statistic\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/4246130467.py:768: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/4246130467.py:806: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/4246130467.py:808: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif np.str.upper(sess_name[:3]) == 'WW2':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/4246130467.py:811: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:2] + day_name]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "189 189\n",
      "172 361\n",
      "183 544\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/4246130467.py:770: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif np.str.upper(rat_name) == 'Q':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97 641\n",
      "66 707\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/4246130467.py:772: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif np.str.upper(rat_name) == 'S':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140 847\n"
     ]
    }
   ],
   "source": [
    "spikes_box = {}\n",
    "i0 = 0\n",
    "folder = 'Toroidal_topology_grid_cell_data//' # directory to data\n",
    "for rat_name, day_name, sess_name, mod_name in (('R', 'day2', 'OF', '1'),\n",
    "                                                ('R', 'day2', 'OF', '2'),\n",
    "                                                ('R', 'day2', 'OF', '3'),\n",
    "                                                ('Q', '', 'OF', '1'),\n",
    "                                                ('Q', '', 'OF', '2'),\n",
    "                                                ('S', '', 'OF', '1')):\n",
    "    spikes_box_temp = get_spikes(rat_name, mod_name, day_name, sess_name, \n",
    "                                 bBinned = False, bSpeed = True, bStart = True, folder = folder)\n",
    "    for i in range(len(spikes_box_temp)):\n",
    "        spikes_box[i0+i] = spikes_box_temp[i]\n",
    "    i0 += len(spikes_box_temp)\n",
    "    print(len(spikes_box_temp), i0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute temporal autocorrelograms for all neurons "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "acorr_OF = get_isi_acorr(spikes_box, bLog = False, bOne = True, maxt = 0.2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Process autocorrelograms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "acorr_scaled_OF = np.zeros(acorr_OF.shape)\n",
    "for i in range(len(acorr_scaled_OF[:,0])):\n",
    "    acorr_scaled_OF[i,:] = acorr_OF[i,:].astype(float).copy()/float(acorr_OF[i,0])\n",
    "acorr_scaled_OF[:,0] = 0\n",
    "acorr_s = gaussian_filter1d(acorr_scaled_OF[:, :],sigma = 4, axis = 1)\n",
    "metric = 'cosine'\n",
    "inds_spec = np.arange(len(acorr_s[:,0]))\n",
    "X1 = squareform(pdist(acorr_s, 'cosine'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Tomato to cluster the autocorrelograms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\anaconda3\\lib\\site-packages\\gudhi\\clustering\\tomato.py:285: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"ro\" (-> color='r'). The keyword argument will take precedence.\n",
      "  plt.plot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bincount [523 229  95]\n"
     ]
    }
   ],
   "source": [
    "nbs = 80\n",
    "knn_indices = np.argsort(X1)[:, :nbs]\n",
    "knn_dists = X1[np.arange(X1.shape[0])[:, None], knn_indices].copy()\n",
    "F = np.sum(np.exp(-knn_dists),1)\n",
    "from gudhi.clustering.tomato import Tomato\n",
    "t = Tomato(graph_type = 'manual', density_type = 'manual', metric = 'precomputed')\n",
    "t.fit(knn_indices, weights = F)\n",
    "t.plot_diagram()\n",
    "t.n_clusters_=4\n",
    "ind = t.labels_\n",
    "bind = np.bincount(ind)\n",
    "print('bincount', bind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 188.5, 188.5, -0.5)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "X4 = X1[:189,:]\n",
    "X4 = X4[:,:189]\n",
    "X4 = X4[np.argsort(ind[:189]),:].copy()\n",
    "X4 = X4[:,np.argsort(ind[:189])]\n",
    "plt.imshow(X4, cmap = 'afmhot')\n",
    "plt.axis('off')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:768: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:770: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif np.str.upper(rat_name) == 'Q':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:772: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif np.str.upper(rat_name) == 'S':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:806: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:807: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_sleep' + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:887: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) == 'SWS':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:921: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  start = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,0]*res\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:924: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  end = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,1]*res\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27544079ee0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spk = get_spikes('S', '1', '', 'SWS', bSmooth = True, bSpeed = False, folder = folder)\n",
    "X1 = np.corrcoef(spk.T)\n",
    "X4 = X1[np.argsort(ind[-140:]),:]\n",
    "X4 = X4[:,np.argsort(ind[-140:])]\n",
    "plt.figure()\n",
    "corrall =  np.sort(X4[np.triu_indices(X4.shape[0],1)])\n",
    "indmax = int(len(corrall)*0.99)\n",
    "vmax = corrall[indmax]\n",
    "vmin = 0\n",
    "plt.imshow(X4, cmap = 'afmhot', vmin = vmin, vmax = vmax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "indscurr1 = ind[:189]\n",
    "indscurr = np.argsort(indscurr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:768: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:806: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:807: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_sleep' + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:887: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) == 'SWS':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:921: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  start = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,0]*res\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3427024557.py:924: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  end = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,1]*res\n"
     ]
    }
   ],
   "source": [
    "spk = get_spikes('R', '1', 'day2', 'SWS', bSmooth = True, bSpeed = False, folder = folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x275441eb310>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1 = np.corrcoef(spk.T)\n",
    "X4 = X1[indscurr,:]\n",
    "X4 = X4[:,indscurr]\n",
    "plt.figure()\n",
    "corrall =  np.sort(X4[np.triu_indices(X4.shape[0],1)])\n",
    "indmax = int(len(corrall)*0.99)\n",
    "#indmin = int(len(corrall)*0.01)\n",
    "vmax = corrall[indmax]\n",
    "#vmin = corrall[indmin]\n",
    "vmin = 0\n",
    "plt.imshow(X4, cmap = 'afmhot', vmin = vmin, vmax = vmax)\n",
    "plt.axis('off')\n",
    "plt.colorbar()\n",
    "#plt.savefig('Figs_review/corrcoef_S1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cs = ['#1f77b4', '#ff7f0e', '#2ca02c']\n",
    "bind = np.bincount(ind)\n",
    "#cs = ['b', 'g', 'r']\n",
    "ax = plt.axes()\n",
    "for i in np.unique(ind):\n",
    "    acorrtmp = acorr_s[np.where(ind==i)[0],:].T\n",
    "    acorrmean = acorrtmp.mean(1)\n",
    "    acorrstd = 1*acorrtmp.std(1)/np.sqrt(len(acorrtmp[:,0]))\n",
    "    ax.plot(acorrmean, lw = 2, c= cs[i], alpha = 1, label = 'Class ' + str(i+1))\n",
    "    ax.fill_between(np.arange(len(acorrmean)),acorrmean, acorrmean + acorrstd,\n",
    "                    lw = 0, color= cs[i], alpha = 0.3)\n",
    "    ax.fill_between(np.arange(len(acorrmean)),acorrmean, acorrmean - acorrstd,\n",
    "                    lw = 0, color= cs[i], alpha = 0.3)\n",
    "    ax.set_aspect(1/ax.get_data_ratio())\n",
    "plt.xticks([0,50,100,150,200], ['', '', '', '',''])\n",
    "plt.yticks(np.arange(0,3,1)/100, ['','',''])\n",
    "plt.gca().axes.spines['top'].set_visible(False)\n",
    "plt.gca().axes.spines['right'].set_visible(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = np.load('is_conjunctive_all.npz', allow_pickle = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "indsall = []\n",
    "for rat_name, day_name, sess_name, mod_name in (('R', 'day2', 'OF', '1'),\n",
    "                                                ('R', 'day2', 'OF', '2'),\n",
    "                                                ('R', 'day2', 'OF', '3'),\n",
    "                                                ('Q', '', 'OF', '1'),\n",
    "                                                ('Q', '', 'OF', '2'),\n",
    "                                                ('S', '', 'OF', '1')):\n",
    "    if len(day_name)>0:\n",
    "        indsall.extend(f['is_conj_' + rat_name + mod_name + '_' + day_name])\n",
    "    else:\n",
    "        indsall.extend(f['is_conj_' + rat_name + mod_name])\n",
    "f.close()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "classes = np.unique(ind)\n",
    "mods = np.unique(indsall).astype(int)\n",
    "distrs = np.zeros((len(classes), len(mods)))\n",
    "for i in classes: \n",
    "    for j in mods:\n",
    "        distrs[i,j] = np.sum((indsall==j) & (ind==i))/sum((indsall==j))\n",
    "\n",
    "cs = ['#1f77b4', '#ff7f0e', '#2ca02c']\n",
    "for i in classes:\n",
    "    for j in mods:\n",
    "        if j%2 ==1:\n",
    "            plt.bar(np.arange(int(j/2)+8,int(j/2)+1+8), distrs[i,j],width = 0.4, \n",
    "                    bottom = np.sum(distrs[:i+1,j])-distrs[i,j],\n",
    "                edgecolor = [[0.,0.,0.]],  lw = 0.5, color = [cs[i]])\n",
    "        else:\n",
    "            plt.bar(np.arange(int(j/2),int(j/2)+1), distrs[i,j],width = 0.4, \n",
    "                    bottom = np.sum(distrs[:i+1,j])-distrs[i,j],\n",
    "                edgecolor = [[0.,0.,0.]],  lw = 0.5, color = [cs[i]])\n",
    "plt.ylim([0,1.01])\n",
    "plt.xlim([-1,14])\n",
    "plt.xticks(np.concatenate((np.arange(6), np.arange(6)+8)),['' for i in np.concatenate((np.arange(6), np.arange(6)+8))])\n",
    "plt.yticks([],[])\n",
    "plt.yticks(np.arange(0,12,2)/10, ['' for i in np.arange(0,12,2)])\n",
    "plt.gca().axes.spines['top'].set_visible(False)\n",
    "plt.gca().axes.spines['right'].set_visible(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "xt = np.log10(np.arange(3,61,2)/1000)\n",
    "pws = [0.5,1,1.5]\n",
    "#xt1 = np.log10(np.power(10,pws)/1000)\n",
    "#xt = np.concatenate((xt1, xt))\n",
    "yt = np.zeros(len(xt)).astype(str)\n",
    "yt[:] = ''\n",
    "yt[0] = '$10^{0.5}$'\n",
    "yt[1] = '$10^1$'\n",
    "yt[2] = '$10^{1.5}$'\n",
    "\n",
    "\n",
    "tot_path = 'acorrpeak_spkwidth.mat'\n",
    "marvin = sio.loadmat(tot_path)\n",
    "rat_mod_name = marvin['mod_name'][0,:]\n",
    "ids = marvin['ids'][0,:]\n",
    "acorr_peak = np.array(marvin['acorr_peak'][0,:])\n",
    "spk_width =  np.array(marvin['spk_width'][0,:])\n",
    "is_conj =  np.array(marvin['is_conj'][0,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "classname = {}\n",
    "classcurr = 0\n",
    "cc = 0\n",
    "for rat_name, sess_name in (('roger', 'box_rec2'), \n",
    "                            ('quentin', 'box'), \n",
    "                            ('shane', 'box')):\n",
    "    for mod_name in ('mod1', 'mod2', 'mod3'):\n",
    "        if (rat_name == 'shane') & (mod_name in ('mod2','mod3')):\n",
    "            continue\n",
    "        if (rat_name == 'quentin') & (mod_name in ('mod3')):\n",
    "            continue\n",
    "        classname[classcurr] = rat_name + '_' + mod_name\n",
    "        classcurr += 1\n",
    "        classname[classcurr] = rat_name + '_' + mod_name + '_conj'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/1961663233.py:3: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  i_name = np.str.upper(i[0][0]) + i[0][-1]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "indsBU1 = np.zeros(len(rat_mod_name), dtype=int)\n",
    "for i in np.unique(rat_mod_name):\n",
    "    i_name = np.str.upper(i[0][0]) + i[0][-1]\n",
    "    for j, j_name in enumerate(('R1', 'R2', 'R3', 'Q1', 'Q2', 'S1')):\n",
    "        if i_name == j_name:\n",
    "            indsBU1[np.where(rat_mod_name == i[0])[0]] = j           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "indssort = np.zeros_like(indsBU1)\n",
    "k = 0\n",
    "for i in np.unique(indsBU1):\n",
    "    indssort[np.arange(k, k+sum(indsBU1==i))] = np.where(indsBU1==i)[0]\n",
    "    k += sum(indsBU1==i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "for i in np.unique(ind):\n",
    "    ax.scatter(np.log10(acorr_peak[indssort][ind==i]), spk_width[indssort][ind==i]*1000, s = 5, alpha = 0.6,\n",
    "              label = str(i))\n",
    "plt.xticks(xt, ['' for x in xt])#yt)\n",
    "\n",
    "ax.set_xlim(xt[0], xt[-1])\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "plt.yticks(np.linspace(0,1,6), ['' for x in np.linspace(0,1,6)])#yt)\n",
    "\n",
    "ax.set_aspect(1/ax.get_data_ratio())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "cols = ['k', 'r']\n",
    "for i in np.unique(is_conj):\n",
    "    ax.scatter(np.log10(acorr_peak[indssort][is_conj[indssort]==i]), spk_width[indssort][is_conj[indssort]==i]*1000, \n",
    "               s = 5, alpha = 0.6, label = str(i), c = cols[i])\n",
    "\n",
    "plt.xticks(xt, ['' for x in xt])#yt)\n",
    "\n",
    "ax.set_xlim(xt[0], xt[-1])\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "plt.yticks(np.linspace(0,1,6), ['' for x in np.linspace(0,1,6)])#yt)\n",
    "\n",
    "ax.set_aspect(1/ax.get_data_ratio())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez(folder + 'Results/grid_cell_classes_indices', \n",
    "         R1_ind = ind[:189],\n",
    "         R2_ind = ind[189:361],\n",
    "         R3_ind = ind[361:544],\n",
    "         Q1_ind = ind[544:641],\n",
    "         Q2_ind = ind[641:707],\n",
    "         S1_ind = ind[707:],         \n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute Bursty R1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "rat_name, mod_name, sess_name, day_name = ('R', '1',  'SWS_bursty', 'day2',)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:769: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:799: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ind = f[np.str.upper(rat_name) + mod_name + '_ind']\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:807: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:808: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_sleep' + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:888: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) == 'SWS':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:922: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  start = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,0]*res\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:925: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  end = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,1]*res\n"
     ]
    }
   ],
   "source": [
    "bRoll = False\n",
    "dim = 6\n",
    "ph_classes = [0,1] # Decode the ith most persistent cohomology class\n",
    "num_circ = len(ph_classes)\n",
    "dec_tresh = 0.99\n",
    "metric = 'cosine'\n",
    "maxdim = 1\n",
    "coeff = 47\n",
    "active_times = 15000\n",
    "k = 1000\n",
    "num_times = 5\n",
    "n_points = 1200\n",
    "nbs = 800\n",
    "sspikes = get_spikes(rat_name, mod_name, day_name, sess_name,  bSmooth = True, bSpeed = False, folder = folder)\n",
    "num_neurons = len(sspikes[0,:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/143927633.py:21: RuntimeWarning: divide by zero encountered in log\n",
      "  d = -np.log(d)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADnCAYAAAC9roUQAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAAsTAAALEwEAmpwYAAAEnElEQVR4nO3dXW7UWBhF0esWM2pmxjSamTGnywNCLQLlJMS1/bfWa0nIT4fPW5ayzDkHAI1/9n4AgDsxugAhowsQMroAIaMLEDK6ACGjCxAyugAhowsQMroAoU9rPy7LMsd//1bPAnAJ88u35dFvLl2AkNEFCK3mhT2sneUAZ+fSBQgZXYDQ7nlBTgDuxKULEDK6AKFd8oKkANyVSxcgZHQBQmlekBWAu3PpAoSMLkAoyQuyAsAPLl2AkNEFCBldgJDRBQgZXYDQ00fXlwsA/3PpAoSMLkDoqaMrLQD8yqULEDK6ACGjCxAyugAhowsQetro+nIB4HcuXYDQMud8/OOyzDmnixVgI6/+5Yjl6+fHq7xCXgD4nbwAEJIXAELyAkBIXgAIyQsAIXkBICQvAITkBYDQU/KCtADwZ/ICQEheAAjJCwAheQEgJC8AhOQFgJC8ABCSFwBC8gJASF4ACMkLACF5ASAkLwCE5AWA0OZ5QVoAeExeAAjJCwAheQEgJC8AhOQFgJC8ABCSFwBC8gJASF4ACMkLACF5ASC0aV6QFgDWyQsAIXkBICQvAITkBYCQvAAQkhcAQvICQEheAAjJCwAheQEgJC8AhOQFgJC8ABCSFwBC8gJASF4ACMkLACF5ASAkLwCE5AWA0Ot5YVnenBcAGGPtWF29dAHYlqYLEDK6ACGjCxAyugAhowsQMroAIaMLEDK6ACGjCxAyugAhowsQMroAIaMLEDK6ACGjCxAyugAhowsQMroAIaMLEDK6ACGjCxAyugAhowsQMroAIaMLEDK6AKHV0V2WZVYPAnAHnz76DyxfPxtm4JDml2/L3s/wkrwAEFrmfHyoLssy55yH+58C4KzkBXinI76ych7yAkBIXgAIXTIveP0DjkpeAAjJCwChS+aFl+QG4CjkBYCQvAAQ+nBeGKNPDHIBcFbyAkBIXgAInTIvXJ18AtclLwCE5AWA0K3zgtd4oCYvAITkBYDQrfPCR8kTwHvJCwAheQEgdOq84PUeOBt5ASAkLwCENskLY5zzCwZ5AqjJCwAheQEgdOu8cFayCJyXvAAQkhcAQvLCk0gAwJ/ICwAheQEgJC+cmIQB5yMvAITkBYDQJfKC12zgLOQFgJC8ABC6RF74W7IEUJMXAELyAkBos7wwxjkTw1vIEMBW5AWAkLwAELpNXpAIgCOQFwBC8gJAaNO8MMaxE8NrJAjg2eQFgJC8ABC6ZF6QCYCjkhcAQvICQOiSeeER2QHYm7wAEJIXAEKb54Ux9k8MMgJwVPICQEheAAg9JS+MsX9iOCLZA5AXAELyAkDoFnnBaz1wFPICQEheAAg9LS+McazE8JLkAOxBXgAIyQsAoafmhZ/2zAwyAnAk8gJASF4ACCV5YYzjfckgOwB7kBcAQvICQCjLCz8dLTNsRa4A3kJeAAjJCwChPC+McczEIA8ABXkBICQvAIR2yQvvccQU8RqpAnhEXgAIyQsAocPnhb91tiwhScA9yAsAoVfzQvgsAJewlmVXRxeAbckLACGjCxAyugAhowsQMroAIaMLEDK6ACGjCxAyugAhowsQ+g4zd0eD9TEcOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "times_cube = np.arange(0,len(sspikes[:,0]),num_times)\n",
    "movetimes = np.sort(np.argsort(np.sum(sspikes[times_cube,:],1))[-active_times:])\n",
    "movetimes = times_cube[movetimes]\n",
    "\n",
    "dim_red_spikes_move_scaled,__,__ = pca(preprocessing.scale(sspikes[movetimes,:]), dim = dim)\n",
    "indstemp,dd,fs  = sample_denoising(dim_red_spikes_move_scaled,  k, \n",
    "                                    n_points, 1, metric)\n",
    "dim_red_spikes_move_scaled = dim_red_spikes_move_scaled[indstemp,:]\n",
    "X = squareform(pdist(dim_red_spikes_move_scaled, metric))\n",
    "knn_indices = np.argsort(X)[:, :nbs]\n",
    "knn_dists = X[np.arange(X.shape[0])[:, None], knn_indices].copy()\n",
    "sigmas, rhos = smooth_knn_dist(knn_dists, nbs, local_connectivity=0)\n",
    "rows, cols, vals = compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos)\n",
    "result = coo_matrix((vals, (rows, cols)), shape=(X.shape[0], X.shape[0]))\n",
    "result.eliminate_zeros()\n",
    "transpose = result.transpose()\n",
    "prod_matrix = result.multiply(transpose)\n",
    "result = (result + transpose - prod_matrix)\n",
    "result.eliminate_zeros()\n",
    "d = result.toarray()\n",
    "d = -np.log(d)\n",
    "np.fill_diagonal(d,0)\n",
    "\n",
    "persistence = ripser(d, maxdim=maxdim, coeff=coeff, do_cocycles= True, distance_matrix = True)\n",
    "\n",
    "plot_barcode(persistence['dgms'])\n",
    "\n",
    "np.savez_compressed(folder + 'Results/' + \n",
    "    rat_name + '_' + mod_name + '_' + sess_name  + day_name  + '_persistence', \n",
    "    persistence = persistence, indstemp = indstemp,  movetimes = movetimes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:633: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
      "  v1[i, :] = [i, np.where(verts == edges[0][i])[0]]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:634: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
      "  v2[i, :] = [i, np.where(verts == edges[1][i])[0]]\n"
     ]
    }
   ],
   "source": [
    "############ Decode cocycles ################\n",
    "diagrams = persistence[\"dgms\"] # the multiset describing the lives of the persistence classes\n",
    "cocycles = persistence[\"cocycles\"][1] # the cocycle representatives for the 1-dim classes\n",
    "dists_land = persistence[\"dperm2all\"] # the pairwise distance between the points \n",
    "births1 = diagrams[1][:, 0] #the time of birth for the 1-dim classes\n",
    "deaths1 = diagrams[1][:, 1] #the time of death for the 1-dim classes\n",
    "deaths1[np.isinf(deaths1)] = 0\n",
    "lives1 = deaths1-births1 # the lifetime for the 1-dim classes\n",
    "iMax = np.argsort(lives1)\n",
    "coords1 = np.zeros((num_circ, len(indstemp)))\n",
    "threshold = births1[iMax[-2]] + (deaths1[iMax[-2]] - births1[iMax[-2]])*dec_tresh\n",
    "for c in ph_classes:\n",
    "    cocycle = cocycles[iMax[-(c+1)]]\n",
    "    coords1[c,:],inds = get_coords(cocycle, threshold, len(indstemp), dists_land, coeff)\n",
    "\n",
    "num_neurons = len(sspikes[0,:])\n",
    "centcosall = np.zeros((num_neurons, 2, n_points))\n",
    "centsinall = np.zeros((num_neurons, 2, n_points))\n",
    "\n",
    "dspk = preprocessing.scale(sspikes[movetimes[indstemp],:])\n",
    "\n",
    "for neurid in range(num_neurons):\n",
    "    spktemp = dspk[:, neurid].copy()\n",
    "    centcosall[neurid,:,:] = np.multiply(np.cos(coords1[:, :]*2*np.pi),spktemp)\n",
    "    centsinall[neurid,:,:] = np.multiply(np.sin(coords1[:, :]*2*np.pi),spktemp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:769: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:799: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ind = f[np.str.upper(rat_name) + mod_name + '_ind']\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:807: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:808: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_sleep' + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:888: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) == 'SWS':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:922: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  start = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,0]*res\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:925: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  end = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,1]*res\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:769: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:799: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ind = f[np.str.upper(rat_name) + mod_name + '_ind']\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:807: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:809: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif np.str.upper(sess_name[:3]) == 'WW2':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:812: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:2] + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:888: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) == 'SWS':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:992: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1007: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1010: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1045: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if (np.str.upper(rat_name) == 'R') & (day_name == 'day1'):\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1067: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if (np.str.upper(rat_name) =='S') & (sess_name == 'WW'):\n"
     ]
    }
   ],
   "source": [
    "sigma = 1500\n",
    "sspikes = get_spikes(rat_name, mod_name, day_name, sess_name , bSmooth = True, bSpeed = False,\n",
    "                     smoothing_width = sigma, folder = folder)\n",
    "spikes = get_spikes(rat_name, mod_name, day_name, sess_name, bSmooth = False, bSpeed = False,\n",
    "                   folder = folder)\n",
    "\n",
    "times = np.where(np.sum(spikes>0, 1)>=1)[0]\n",
    "dspk = preprocessing.scale(sspikes)\n",
    "sspikes = sspikes[times,:]\n",
    "dspk = dspk[times,:]\n",
    "\n",
    "a = np.zeros((len(sspikes[:,0]), 2, num_neurons))\n",
    "for n in range(num_neurons):\n",
    "    a[:,:,n] = np.multiply(dspk[:,n:n+1],np.sum(centcosall[n,:,:],1))\n",
    "\n",
    "c = np.zeros((len(sspikes[:,0]), 2, num_neurons))\n",
    "for n in range(num_neurons):\n",
    "    c[:,:,n] = np.multiply(dspk[:,n:n+1],np.sum(centsinall[n,:,:],1))\n",
    "\n",
    "mtot2 = np.sum(c,2)\n",
    "mtot1 = np.sum(a,2)\n",
    "coords = np.arctan2(mtot2,mtot1)%(2*np.pi)\n",
    "\n",
    "sspikes_of,__,__,__,__ = get_spikes(rat_name, mod_name, day_name, 'OF_bursty',\n",
    "                                 bSmooth = True, bSpeed = True, smoothing_width = sigma, folder = folder)\n",
    "\n",
    "spikes_of,__,__,__,__ = get_spikes(rat_name, mod_name, day_name, 'OF_bursty',\n",
    "                                 bSmooth = False, bSpeed = True, folder = folder)\n",
    "\n",
    "dspk =preprocessing.scale(sspikes_of)\n",
    "times_box = np.where(np.sum(spikes_of>0, 1)>=1)[0]\n",
    "dspk = dspk[times_box,:]\n",
    "\n",
    "a = np.zeros((len(times_box), 2, num_neurons))\n",
    "for n in range(num_neurons):\n",
    "    a[:,:,n] = np.multiply(dspk[:,n:n+1],np.sum(centcosall[n,:,:],1))\n",
    "\n",
    "c = np.zeros((len(times_box), 2, num_neurons))\n",
    "for n in range(num_neurons):\n",
    "    c[:,:,n] = np.multiply(dspk[:,n:n+1],np.sum(centsinall[n,:,:],1))\n",
    "\n",
    "mtot2 = np.sum(c,2)\n",
    "mtot1 = np.sum(a,2)\n",
    "coordsbox = np.arctan2(mtot2,mtot1)%(2*np.pi)\n",
    "file_name = rat_name + '_' + mod_name + '_' + sess_name\n",
    "if len(day_name)>0:\n",
    "    file_name += '_' + day_name\n",
    "np.savez_compressed(folder + 'Results/' + file_name  + '_decoding', \n",
    "                    coords = coords, coordsbox = coordsbox,  times = times, \n",
    "                    times_box = times_box, centcosall = centcosall, centsinall = centsinall)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:992: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1007: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1010: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1045: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if (np.str.upper(rat_name) == 'R') & (day_name == 'day1'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1067: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if (np.str.upper(rat_name) =='S') & (sess_name == 'WW'):\n"
     ]
    }
   ],
   "source": [
    "rat_name, mod_name, sess_name, day_name = ('R', '1', 'SWS_bursty', 'day2')\n",
    "fit_para(rat_name, mod_name, sess_name, day_name, folder = folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:992: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1007: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1010: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1045: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if (np.str.upper(rat_name) == 'R') & (day_name == 'day1'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/2043634716.py:1067: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if (np.str.upper(rat_name) =='S') & (sess_name == 'WW'):\n"
     ]
    }
   ],
   "source": [
    "rat_name, mod_name, sess_name, day_name = ('R', '1', 'OF', 'day2')\n",
    "fit_para(rat_name, mod_name, sess_name, day_name, folder = folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120388, 2)\n",
      "(120388, 2)\n"
     ]
    }
   ],
   "source": [
    "rat_name, mod_name,  sess_name_0, sess_name_1, day_name = ('R', '1', 'SWS_bursty', 'OF', 'day2')\n",
    "toroidal_alignment(rat_name, mod_name, sess_name_0, sess_name_1, day_name, bPlot = True, folder = folder)\n",
    "toroidal_alignment(rat_name, mod_name,  sess_name_1, sess_name_1, day_name, bPlot = False, folder = folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cumulative distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3763907366.py:769: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(rat_name) == 'R':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3763907366.py:799: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  ind = f[np.str.upper(rat_name) + mod_name + '_ind']\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3763907366.py:807: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  if np.str.upper(sess_name[:3]) in ('SWS', 'REM'):\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3763907366.py:809: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  elif np.str.upper(sess_name[:3]) == 'WW2':\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3763907366.py:812: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:2] + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3763907366.py:808: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  times = times_all['rat_' + np.str.lower(rat_name) + '_sleep' + day_name]\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3763907366.py:922: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  start = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,0]*res\n",
      "C:\\Users\\Finnern\\AppData\\Local\\Temp/ipykernel_20176/3763907366.py:925: DeprecationWarning: `np.str` is a deprecated alias for the builtin `str`. To silence this warning, use `str` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.str_` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  end = np.array(times_all['rat_' + np.str.lower(rat_name) + '_' + sess_name[:3] + day_name])[:,1]*res\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "file_name =   rat_name + '_' + mod_name + '_' + sess_name_0 + '_' + sess_name_1 + '_' + day_name\n",
    "f = np.load(folder + 'Results/' + file_name + '_alignment_dec.npz', allow_pickle = True)\n",
    "cc_sws = f['csess']\n",
    "times_sws = f['times']\n",
    "f.close()\n",
    "            \n",
    "file_name =   rat_name + '_' + mod_name + '_' + sess_name_1 + '_' + sess_name_1 + '_' + day_name\n",
    "f = np.load(folder + 'Results/' + file_name + '_alignment_dec.npz', allow_pickle = True)\n",
    "cc_of = f['csess']\n",
    "times_of = f['times']\n",
    "f.close()\n",
    "\n",
    "for k in ['_bursty', '_theta', '_nonbursty']:\n",
    "    spk_of = get_spikes('R', '1', 'day2', 'OF' + k,  bSmooth = False, bSpeed = False, folder = folder)\n",
    "    spk_sws = get_spikes('R', '1', 'day2', 'SWS' + k, bSmooth = False, bSpeed = False, folder = folder)\n",
    "\n",
    "    mtots_of, mcs_of = get_ratemaps(cc_of, spk_of[times_of,:])\n",
    "    mtots_sws, mcs_sws = get_ratemaps(cc_sws, spk_sws[times_sws,:])\n",
    "    \n",
    "    corr, dist, corr_shuffle, dist_shuffle = get_corr_dist(mcs_of,mcs_sws, mtots_of, mtots_sws)\n",
    "    \n",
    "    plot_cumulative_stat(corr, corr_shuffle, (-1,1), 1, \n",
    "                         [-1, -0.5, 0.0, 0.5, 1.0], [0.0, 0.25, 0.5, 0.75, 1.0], [-1,1], [-0.05,1.05])\n",
    "    plot_cumulative_stat(dist, dist_shuffle, (0,np.sqrt(2)*np.pi), 180/np.pi, \n",
    "                         [0, 62.5, 125, 187.5, 250], [0.0, 0.25, 0.5, 0.75, 1.0], [0,255], [-0.05,1.05])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Conjunctive HD tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [],
   "source": [
    "rat_name, mod_name, sess_name, day_name = ('R', '1',  'SWS_theta', 'day2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\erihe\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:37: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bRoll = False\n",
    "dim = 6\n",
    "ph_classes = [0,1] # Decode the ith most persistent cohomology class\n",
    "num_circ = len(ph_classes)\n",
    "dec_tresh = 0.99\n",
    "metric = 'cosine'\n",
    "maxdim = 1\n",
    "coeff = 47\n",
    "active_times = 15000\n",
    "k = 1000\n",
    "num_times = 5\n",
    "n_points = 1200\n",
    "nbs = 800\n",
    "\n",
    "sspikes = get_spikes(rat_name, mod_name, day_name, sess_name,  bSmooth = True, bSpeed = False, folder = folder)\n",
    "num_neurons = len(sspikes[0,:])\n",
    "\n",
    "times_cube = np.arange(0,len(sspikes[:,0]),num_times)\n",
    "movetimes = np.sort(np.argsort(np.sum(sspikes[times_cube,:],1))[-active_times:])\n",
    "movetimes = times_cube[movetimes]\n",
    "\n",
    "dim_red_spikes_move_scaled,__,__ = pca(preprocessing.scale(sspikes[movetimes,:]), dim = dim)\n",
    "indstemp,dd,fs  = sample_denoising(dim_red_spikes_move_scaled,  k, \n",
    "                                    n_points, 1, metric)\n",
    "dim_red_spikes_move_scaled = dim_red_spikes_move_scaled[indstemp,:]\n",
    "X = squareform(pdist(dim_red_spikes_move_scaled, metric))\n",
    "knn_indices = np.argsort(X)[:, :nbs]\n",
    "knn_dists = X[np.arange(X.shape[0])[:, None], knn_indices].copy()\n",
    "sigmas, rhos = smooth_knn_dist(knn_dists, nbs, local_connectivity=0)\n",
    "rows, cols, vals = compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos)\n",
    "result = coo_matrix((vals, (rows, cols)), shape=(X.shape[0], X.shape[0]))\n",
    "result.eliminate_zeros()\n",
    "transpose = result.transpose()\n",
    "prod_matrix = result.multiply(transpose)\n",
    "result = (result + transpose - prod_matrix)\n",
    "result.eliminate_zeros()\n",
    "d = result.toarray()\n",
    "d = -np.log(d)\n",
    "np.fill_diagonal(d,0)\n",
    "\n",
    "persistence = ripser(d, maxdim=maxdim, coeff=coeff, do_cocycles= True, distance_matrix = True)\n",
    "\n",
    "plot_barcode(persistence['dgms'])\n",
    "\n",
    "np.savez_compressed(folder + 'Results/' + \n",
    "    rat_name + '_' + mod_name + '_' + sess_name + '_theta_'  + day_name  + '_persistence', \n",
    "    persistence = persistence, indstemp = indstemp,  movetimes = movetimes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [],
   "source": [
    "############ Decode cocycles ################\n",
    "diagrams = persistence[\"dgms\"] # the multiset describing the lives of the persistence classes\n",
    "cocycles = persistence[\"cocycles\"][1] # the cocycle representatives for the 1-dim classes\n",
    "dists_land = persistence[\"dperm2all\"] # the pairwise distance between the points \n",
    "births1 = diagrams[1][:, 0] #the time of birth for the 1-dim classes\n",
    "deaths1 = diagrams[1][:, 1] #the time of death for the 1-dim classes\n",
    "deaths1[np.isinf(deaths1)] = 0\n",
    "lives1 = deaths1-births1 # the lifetime for the 1-dim classes\n",
    "iMax = np.argsort(lives1)\n",
    "coords1 = np.zeros((num_circ, len(indstemp)))\n",
    "threshold = births1[iMax[-2]] + (deaths1[iMax[-2]] - births1[iMax[-2]])*dec_tresh\n",
    "for c in ph_classes:\n",
    "    cocycle = cocycles[iMax[-(c+1)]]\n",
    "    coords1[c,:],inds = get_coords(cocycle, threshold, len(indstemp), dists_land, coeff)\n",
    "\n",
    "num_neurons = len(sspikes[0,:])\n",
    "centcosall = np.zeros((num_neurons, 2, n_points))\n",
    "centsinall = np.zeros((num_neurons, 2, n_points))\n",
    "\n",
    "dspk = preprocessing.scale(sspikes[movetimes[indstemp],:])\n",
    "\n",
    "for neurid in range(num_neurons):\n",
    "    spktemp = dspk[:, neurid].copy()\n",
    "    centcosall[neurid,:,:] = np.multiply(np.cos(coords1[:, :]*2*np.pi),spktemp)\n",
    "    centsinall[neurid,:,:] = np.multiply(np.sin(coords1[:, :]*2*np.pi),spktemp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = 100000\n",
    "sspikes_of,__,__,__,__ = get_spikes(rat_name, mod_name, day_name, 'OF_theta',\n",
    "                                 bSmooth = True, bSpeed = True, smoothing_width = sigma, folder = folder)\n",
    "\n",
    "dspk =preprocessing.scale(sspikes_of)\n",
    "\n",
    "a = np.zeros((len(dspk[:,0]), 2, num_neurons))\n",
    "for n in range(num_neurons):\n",
    "    a[:,:,n] = np.multiply(dspk[:,n:n+1],np.sum(centcosall[n,:,:],1))\n",
    "\n",
    "c = np.zeros((len(dspk[:,0]), 2, num_neurons))\n",
    "for n in range(num_neurons):\n",
    "    c[:,:,n] = np.multiply(dspk[:,n:n+1],np.sum(centsinall[n,:,:],1))\n",
    "\n",
    "mtot2 = np.sum(c,2)\n",
    "mtot1 = np.sum(a,2)\n",
    "coordsbox = np.arctan2(mtot2,mtot1)%(2*np.pi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8576425411784621\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a089be0d88>"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx, yy, aa, tt, speed = load_pos(rat_name, 'OF', day_name, bSpeed = True, folder = folder)\n",
    "\n",
    "tt = tt[speed>2.5]\n",
    "xx = xx[speed>2.5]\n",
    "yy = yy[speed>2.5]\n",
    "aa = aa[speed>2.5]\n",
    "\n",
    "cctmp = coordsbox[:,0].copy()\n",
    "nnans = (~np.isnan(aa)) & (~np.isnan(cctmp[:]))\n",
    "circmin = np.arctan2(np.mean(np.sin(cctmp[nnans]-aa[nnans])),\n",
    "                     np.mean(np.cos(cctmp[nnans]-aa[nnans])))%(2*np.pi)\n",
    "cc1 = (cctmp[:]-circmin)%(2*np.pi)\n",
    "circdiff = np.mean(np.abs(np.arctan2(np.sin(cc1[nnans]-aa[nnans]),\n",
    "                     np.cos(cc1[nnans]-aa[nnans]))))\n",
    "print(circdiff)\n",
    "t0, t1 = 800,10800\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(np.arange(t0,t1), cc1[t0:t1]/(2*np.pi)*360, \n",
    "           lw = 0.0, alpha = 0.7, label ='decoded', s = 10)\n",
    "ax.scatter(np.arange(t0,t1), aa[t0:t1]/(2*np.pi)*360, \n",
    "           lw = 0.0, alpha = 0.7, label ='recorded', s = 10)\n",
    "ax.set_xlim([t0,t1])\n",
    "ax.set_ylim([0,2*np.pi/(2*np.pi)*360])\n",
    "\n",
    "ax.set_yticks([0,180,360])\n",
    "ax.set_xticks([800,5800, 10800])\n",
    "ax.set_yticklabels(['', '', ''])\n",
    "ax.set_xticklabels(['', '', '', ])\n",
    "\n",
    "ax.set_yticklabels(['0$\\degree$', '180$\\degree$', '360$\\degree$'])\n",
    "ax.set_xticklabels(['0s', '5s', '10s', '15s', '20s'])\n",
    "\n",
    "ax.set_aspect(8)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "rat_name, mod_name, sess_name, day_name = ('S', '1',  'SWS_theta', '')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\erihe\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:37: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bRoll = False\n",
    "dim = 6\n",
    "ph_classes = [0,1] # Decode the ith most persistent cohomology class\n",
    "num_circ = len(ph_classes)\n",
    "dec_tresh = 0.99\n",
    "metric = 'cosine'\n",
    "maxdim = 1\n",
    "coeff = 47\n",
    "active_times = 15000\n",
    "k = 1000\n",
    "num_times = 5\n",
    "n_points = 1200\n",
    "nbs = 800\n",
    "\n",
    "sspikes = get_spikes(rat_name, mod_name, day_name, sess_name,  bSmooth = True, bSpeed = False, folder = folder)\n",
    "num_neurons = len(sspikes[0,:])\n",
    "\n",
    "times_cube = np.arange(0,len(sspikes[:,0]),num_times)\n",
    "movetimes = np.sort(np.argsort(np.sum(sspikes[times_cube,:],1))[-active_times:])\n",
    "movetimes = times_cube[movetimes]\n",
    "\n",
    "dim_red_spikes_move_scaled,__,__ = pca(preprocessing.scale(sspikes[movetimes,:]), dim = dim)\n",
    "indstemp,dd,fs  = sample_denoising(dim_red_spikes_move_scaled,  k, \n",
    "                                    n_points, 1, metric)\n",
    "dim_red_spikes_move_scaled = dim_red_spikes_move_scaled[indstemp,:]\n",
    "X = squareform(pdist(dim_red_spikes_move_scaled, metric))\n",
    "knn_indices = np.argsort(X)[:, :nbs]\n",
    "knn_dists = X[np.arange(X.shape[0])[:, None], knn_indices].copy()\n",
    "sigmas, rhos = smooth_knn_dist(knn_dists, nbs, local_connectivity=0)\n",
    "rows, cols, vals = compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos)\n",
    "result = coo_matrix((vals, (rows, cols)), shape=(X.shape[0], X.shape[0]))\n",
    "result.eliminate_zeros()\n",
    "transpose = result.transpose()\n",
    "prod_matrix = result.multiply(transpose)\n",
    "result = (result + transpose - prod_matrix)\n",
    "result.eliminate_zeros()\n",
    "d = result.toarray()\n",
    "d = -np.log(d)\n",
    "np.fill_diagonal(d,0)\n",
    "\n",
    "persistence = ripser(d, maxdim=maxdim, coeff=coeff, do_cocycles= True, distance_matrix = True)\n",
    "\n",
    "plot_barcode(persistence['dgms'])\n",
    "\n",
    "np.savez_compressed(folder + 'Results/' + \n",
    "    rat_name + '_' + mod_name + '_' + sess_name + '_theta_'  + day_name  + '_persistence', \n",
    "    persistence = persistence, indstemp = indstemp,  movetimes = movetimes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "############ Decode cocycles ################\n",
    "diagrams = persistence[\"dgms\"] # the multiset describing the lives of the persistence classes\n",
    "cocycles = persistence[\"cocycles\"][1] # the cocycle representatives for the 1-dim classes\n",
    "dists_land = persistence[\"dperm2all\"] # the pairwise distance between the points \n",
    "births1 = diagrams[1][:, 0] #the time of birth for the 1-dim classes\n",
    "deaths1 = diagrams[1][:, 1] #the time of death for the 1-dim classes\n",
    "deaths1[np.isinf(deaths1)] = 0\n",
    "lives1 = deaths1-births1 # the lifetime for the 1-dim classes\n",
    "iMax = np.argsort(lives1)\n",
    "coords1 = np.zeros((num_circ, len(indstemp)))\n",
    "threshold = births1[iMax[-2]] + (deaths1[iMax[-2]] - births1[iMax[-2]])*dec_tresh\n",
    "for c in ph_classes:\n",
    "    cocycle = cocycles[iMax[-(c+1)]]\n",
    "    coords1[c,:],inds = get_coords(cocycle, threshold, len(indstemp), dists_land, coeff)\n",
    "\n",
    "num_neurons = len(sspikes[0,:])\n",
    "centcosall = np.zeros((num_neurons, 2, n_points))\n",
    "centsinall = np.zeros((num_neurons, 2, n_points))\n",
    "\n",
    "dspk = preprocessing.scale(sspikes[movetimes[indstemp],:])\n",
    "\n",
    "for neurid in range(num_neurons):\n",
    "    spktemp = dspk[:, neurid].copy()\n",
    "    centcosall[neurid,:,:] = np.multiply(np.cos(coords1[:, :]*2*np.pi),spktemp)\n",
    "    centsinall[neurid,:,:] = np.multiply(np.sin(coords1[:, :]*2*np.pi),spktemp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = 100000\n",
    "sspikes_of,__,__,__,__ = get_spikes(rat_name, mod_name, day_name, 'OF_theta',\n",
    "                                 bSmooth = True, bSpeed = True, smoothing_width = sigma, folder = folder)\n",
    "\n",
    "dspk =preprocessing.scale(sspikes_of)\n",
    "\n",
    "a = np.zeros((len(dspk[:,0]), 2, num_neurons))\n",
    "for n in range(num_neurons):\n",
    "    a[:,:,n] = np.multiply(dspk[:,n:n+1],np.sum(centcosall[n,:,:],1))\n",
    "\n",
    "c = np.zeros((len(dspk[:,0]), 2, num_neurons))\n",
    "for n in range(num_neurons):\n",
    "    c[:,:,n] = np.multiply(dspk[:,n:n+1],np.sum(centsinall[n,:,:],1))\n",
    "\n",
    "mtot2 = np.sum(c,2)\n",
    "mtot1 = np.sum(a,2)\n",
    "coordsbox = np.arctan2(mtot2,mtot1)%(2*np.pi)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7439584338002855\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2a08c66a0c8>"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx, yy, aa, tt, speed = load_pos(rat_name, 'OF', day_name, bSpeed = True, folder = folder)\n",
    "\n",
    "tt = tt[speed>2.5]\n",
    "xx = xx[speed>2.5]\n",
    "yy = yy[speed>2.5]\n",
    "aa = aa[speed>2.5]\n",
    "\n",
    "cctmp = 2*np.pi-coordsbox[:,0].copy()\n",
    "nnans = (~np.isnan(aa)) & (~np.isnan(cctmp[:]))\n",
    "circmin = np.arctan2(np.mean(np.sin(cctmp[nnans]-aa[nnans])),\n",
    "                     np.mean(np.cos(cctmp[nnans]-aa[nnans])))%(2*np.pi)\n",
    "cc1 = (cctmp[:]-circmin)%(2*np.pi)\n",
    "circdiff = np.mean(np.abs(np.arctan2(np.sin(cc1[nnans]-aa[nnans]),\n",
    "                     np.cos(cc1[nnans]-aa[nnans]))))\n",
    "print(circdiff)\n",
    "t0, t1 = 800,10800\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(np.arange(t0,t1), cc1[t0:t1]/(2*np.pi)*360, \n",
    "           lw = 0.0, alpha = 0.7, label ='decoded', s = 10)\n",
    "ax.scatter(np.arange(t0,t1), aa[t0:t1]/(2*np.pi)*360, \n",
    "           lw = 0.0, alpha = 0.7, label ='recorded', s = 10)\n",
    "ax.set_xlim([t0,t1])\n",
    "ax.set_ylim([0,2*np.pi/(2*np.pi)*360])\n",
    "\n",
    "ax.set_yticks([0,180,360])\n",
    "ax.set_xticks([800,5800, 10800])\n",
    "ax.set_yticklabels(['', '', ''])\n",
    "ax.set_xticklabels(['', '', '', ])\n",
    "\n",
    "ax.set_yticklabels(['0$\\degree$', '180$\\degree$', '360$\\degree$'])\n",
    "ax.set_xticklabels(['0s', '5s', '10s', '15s', '20s'])\n",
    "\n",
    "ax.set_aspect(8)\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
